Evidence map›Paper›PMID 37645426›Full record

ArticleFrontiers in oncology2023

Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma.

Satheesh Krishna, Andrew Sertic, Zhihui Amy Liu, Zijin Liu, Gail E Darling, Jonathon Yeung, Rebecca Wong, Eric X Chen, Sangeetha Kalimuthu, Michael J Allen and 9 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 4 institutions in 1 country.

Satheesh KrishnaDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Andrew SerticDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Zhihui Amy LiuDepartment of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Zijin LiuDepartment of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Gail E DarlingDivision of Thoracic Oncology, Toronto General Hospital, University Health Network, Toronto, ON, Canada.
Jonathon YeungDivision of Thoracic Oncology, Toronto General Hospital, University Health Network, Toronto, ON, Canada.
Rebecca WongDivision of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada.
Eric X ChenDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Sangeetha KalimuthuDivision of Pathology, Toronto General Hospital, University Health Network, Toronto, ON, Canada.
Michael J AllenDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Chihiro SuzukiDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Elan PanovDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Lucy X MaDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Yvonne BachDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Raymond W JangDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Carol J SwallowDepartment of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Savtaj BrarDepartment of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Elena ElimovaDivision of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Patrick Veit-HaibachDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Princess Margaret Cancer Centre · CAUniversity Health Network · CAUniversity of Toronto · CAToronto General Hospital · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes. Methods: A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8-12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. "Delta" radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and "delta" features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson's correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS. Results: Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan-Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively. Conclusions: A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.

Indexed as

carcinomaesophagealgastricradiomicssurvival

Identifiers

PMID37645426
PMCPMC10461093
OpenAlexW4385803217

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.